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More ARD flags in exponential and Matern32

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Nicolas 2013-01-18 15:34:06 +00:00
parent 11d088cf90
commit 0c30048e57
2 changed files with 40 additions and 25 deletions
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60
GPy/kern/Matern32.py
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@ -20,43 +20,52 @@ class Matern32(kernpart):
:type D: int
:param variance: the variance :math:`\sigma^2`
:type variance: float
:param lengthscale: the lengthscales :math:`\ell_i`
:param lengthscale: the lengthscale :math:`\ell_i`
:type lengthscale: np.ndarray of size (D,)
:rtype: kernel object
"""
def __init__(self,D,variance=1.,lengthscales=None):
def __init__(self,D,variance=1.,lengthscale=None,ARD=False):
self.D = D
if lengthscales is not None:
assert lengthscales.shape==(self.D,)
self.ARD = ARD
if ARD == False:
self.Nparam = 2
self.name = 'Mat32'
if lengthscale is not None:
assert lengthscale.shape == (1,)
else:
lengthscale = np.ones(1)
else:
lengthscales = np.ones(self.D)
self.Nparam = self.D + 1
self.name = 'Mat32'
self._set_params(np.hstack((variance,lengthscales)))
self.Nparam = self.D + 1
self.name = 'Mat32_ARD'
if lengthscale is not None:
assert lengthscale.shape == (self.D,)
else:
lengthscale = np.ones(self.D)
self._set_params(np.hstack((variance,lengthscale)))
def _get_params(self):
"""return the value of the parameters."""
return np.hstack((self.variance,self.lengthscales))
return np.hstack((self.variance,self.lengthscale))
def _set_params(self,x):
"""set the value of the parameters."""
assert x.size==(self.D+1)
assert x.size == self.Nparam
self.variance = x[0]
self.lengthscales = x[1:]
self.lengthscale = x[1:]
def _get_param_names(self):
"""return parameter names."""
if self.D==1:
if self.Nparam == 2:
return ['variance','lengthscale']
else:
return ['variance']+['lengthscale_%i'%i for i in range(self.lengthscales.size)]
return ['variance']+['lengthscale_%i'%i for i in range(self.lengthscale.size)]
def K(self,X,X2,target):
"""Compute the covariance matrix between X and X2."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscales),-1))
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscale),-1))
np.add(self.variance*(1+np.sqrt(3.)*dist)*np.exp(-np.sqrt(3.)*dist), target,target)
def Kdiag(self,X,target):
@ -66,13 +75,20 @@ class Matern32(kernpart):
def dK_dtheta(self,partial,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscales),-1))
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscale),-1))
dvar = (1+np.sqrt(3.)*dist)*np.exp(-np.sqrt(3.)*dist)
invdist = 1./np.where(dist!=0.,dist,np.inf)
dist2M = np.square(X[:,None,:]-X2[None,:,:])/self.lengthscales**3
dl = (self.variance* 3 * dist * np.exp(-np.sqrt(3.)*dist))[:,:,np.newaxis] * dist2M*invdist[:,:,np.newaxis]
dist2M = np.square(X[:,None,:]-X2[None,:,:])/self.lengthscale**3
#dl = (self.variance* 3 * dist * np.exp(-np.sqrt(3.)*dist))[:,:,np.newaxis] * dist2M*invdist[:,:,np.newaxis]
target[0] += np.sum(dvar*partial)
target[1:] += (dl*partial[:,:,None]).sum(0).sum(0)
if self.ARD == True:
dl = (self.variance* 3 * dist * np.exp(-np.sqrt(3.)*dist))[:,:,np.newaxis] * dist2M*invdist[:,:,np.newaxis]
#dl = self.variance*dvar[:,:,None]*dist2M*invdist[:,:,None]
target[1:] += (dl*partial[:,:,None]).sum(0).sum(0)
else:
dl = (self.variance* 3 * dist * np.exp(-np.sqrt(3.)*dist)) * dist2M.sum(-1)*invdist
#dl = self.variance*dvar*dist2M.sum(-1)*invdist
target[1] += np.sum(dl*partial)
def dKdiag_dtheta(self,partial,X,target):
"""derivative of the diagonal of the covariance matrix with respect to the parameters."""
@ -81,8 +97,8 @@ class Matern32(kernpart):
def dK_dX(self,partial,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscales),-1))[:,:,None]
ddist_dX = (X[:,None,:]-X2[None,:,:])/self.lengthscales**2/np.where(dist!=0.,dist,np.inf)
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscale),-1))[:,:,None]
ddist_dX = (X[:,None,:]-X2[None,:,:])/self.lengthscale**2/np.where(dist!=0.,dist,np.inf)
dK_dX = - np.transpose(3*self.variance*dist*np.exp(-np.sqrt(3)*dist)*ddist_dX,(1,0,2))
target += np.sum(dK_dX*partial.T[:,:,None],0)
@ -104,7 +120,7 @@ class Matern32(kernpart):
"""
assert self.D == 1
def L(x,i):
return(3./self.lengthscales**2*F[i](x) + 2*np.sqrt(3)/self.lengthscales*F1[i](x) + F2[i](x))
return(3./self.lengthscale**2*F[i](x) + 2*np.sqrt(3)/self.lengthscale*F1[i](x) + F2[i](x))
n = F.shape[0]
G = np.zeros((n,n))
for i in range(n):
@ -114,5 +130,5 @@ class Matern32(kernpart):
F1lower = np.array([f(lower) for f in F1])[:,None]
#print "OLD \n", np.dot(F1lower,F1lower.T), "\n \n"
#return(G)
return(self.lengthscales**3/(12.*np.sqrt(3)*self.variance) * G + 1./self.variance*np.dot(Flower,Flower.T) + self.lengthscales**2/(3.*self.variance)*np.dot(F1lower,F1lower.T))
return(self.lengthscale**3/(12.*np.sqrt(3)*self.variance) * G + 1./self.variance*np.dot(Flower,Flower.T) + self.lengthscale**2/(3.*self.variance)*np.dot(F1lower,F1lower.T))

5
GPy/kern/exponential.py
View file

@ -49,13 +49,13 @@ class exponential(kernpart):
def _set_params(self,x):
"""set the value of the parameters."""
assert x.size==(self.D+1)
assert x.size == self.Nparam
self.variance = x[0]
self.lengthscale = x[1:]
def _get_param_names(self):
"""return parameter names."""
if self.Nparam==2:
if self.Nparam == 2:
return ['variance','lengthscale']
else:
return ['variance']+['lengthscale_%i'%i for i in range(self.lengthscale.size)]
@ -84,7 +84,6 @@ class exponential(kernpart):
else:
dl = self.variance*dvar*dist2M.sum(-1)*invdist
target[1] += np.sum(dl*partial)
#foo
def dKdiag_dtheta(self,partial,X,target):
"""derivative of the diagonal of the covariance matrix with respect to the parameters."""